Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes
18April2016,update:20April2016, update:23May2016, update:6June16, update:15June16,update:30June16,update:06Nov16, update:18Dec16, update:24Jan17, update:6Feb17
Results for “Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes”
## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
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## lowess## Loading required package: RColorBrewer## Loading required package: data.table## Loading required package: mclust## Package 'mclust' version 5.1## Type 'citation("mclust")' for citing this R package in publications.## Loading required package: ggplot2## Loading required package: gridExtra## Warning: package 'gridExtra' was built under R version 3.2.4## Loading required package: devtools## Loading required package: grid## Loading required package: cluster## Loading required package: plyr## Loading required package: survival##
Read 42.8% of 23368 rows
Read 23368 rows and 1120 (of 1120) columns from 0.361 GB file in 00:00:042(A)-(D):Two dominant phenotypes in breast cancer patients and cell lines
2(A): TCGA , all pathways
#2(A): TCGA , all pathways
create_figure2_a_TCGA_allpath <- function(expression_subtypes_df, showLegends=TRUE, ersubset="Full"){
set.seed(123)
phenotype.annotation <- data.frame(Phenotype=c("Survival Phenotype",
"Growth Phenotype",
"Growth Phenotype",
"Survival Phenotype",
"Survival Phenotype",
"Growth Phenotype",
"Growth Phenotype"))
topha <- HeatmapAnnotation(df = phenotype.annotation,
col = list(Phenotype = c("Survival Phenotype" = "coral3",
"Growth Phenotype" = "aquamarine4")),
height = unit(0.333, "cm"))
maintitle <- "TCGA BRCA"
if(ersubset != "Full"){
expression_subtypes_df <- expression_subtypes_df[expression_subtypes_df$ER.Status == ersubset,]
maintitle <- paste(maintitle,ersubset, sep=", ER ")
}
ha_row_tcga2 = HeatmapAnnotation(df = expression_subtypes_df[,10:13],
col = list(PR.Status = c("Positive" = "#4DAF4A",
"Negative" = "#984EA3",
"Indeterminate" = "black",
"Unavailable" = "grey"),
HER2.Status = c("Positive" = "#FFFF33",
"Negative" = "#F781BF",
"Indeterminate" = "black",
"Equivocal" = "skyblue",
"Unavailable" = "grey"),
ER.Status = c("Positive" = "#E41A1C",
"Negative" = "#377EB8",
"Indeterminate" = "black",
"Unavailable" = "grey"),
PAM50 = c("LumA" = brewer.pal(6, "Dark2")[5],
"LumB" = '#fccaa4',
"Her2" = brewer.pal(6, "Dark2")[4],
"Basal" = '#e41a1c',
"Normal" = brewer.pal(6, "Dark2")[6],
"Unavailable"="grey")),
which = "row",
width = unit(1.333, "cm"),
show_legend = showLegends)
h1 <- Heatmap(expression_subtypes_df[,1:7],
cluster_rows = T,
cluster_columns = T,
show_row_names = F,
show_column_names = T,
row_title_gp = gpar(fontsize =10),
combined_name_fun = NULL,
top_annotation = topha,
name="Scaled\nPathway\nActivity",
column_title = maintitle,
show_heatmap_legend = showLegends,
column_dend_reorder = c(1,100,100,10,1,100,100),
heatmap_legend_param = list(color_bar = "continuous"))
draw(h1+ha_row_tcga2,row_dend_side = "left", annotation_legend_side = "bottom")
}
#uncomment to create PDF version of 2a
# pdf("Fig2a.pdf", width=6)
# create_figure2_a_TCGA_allpath(single_pathway_best_tcga, showLegends = F)
# dev.off()
create_figure2_a_TCGA_allpath(single_pathway_best_tcga)
(B): ICBP, all pathways
(C): TCGA, k-means of 4 pathways
(D): ICBP, k-means of 4 pathways
Unsupervised variance analysis confirms the significance of phenotypes in breast cancer biology
3(A)-(B):Perform Principal Component Analysis
## [1] "Proportion of variance contributed by first 5 principal components in ICBP gene expression data: 0.427196248976044"## [1] "Proportion of variance contributed by first 5 principal components in TCGA BRCA gene expression data: 0.343182614266816"3: Correlate TCGA Principal Components with ASSIGN generated GFRN pathway predictions
4(A)-(E):Survival and growth phenotypes express dichotomous cell survival mechanisms
4(A):Heatmap of breast cancer cell lines from Western Blots
4(C)-(E): Gene and protein expression are significantly different between growth and survival phenotypes
## [1] "733 names have been changed"## [1] "analyzing BIM"
## [1] "BIM data available"
## [1] "analyzing MCL1"
## [1] "Sorry!This protein is unavailable in TCGA RPPA dataset!"## [1] 14
## [1] 15
5(A) &(B): Growth factor network phenotypes reflect dichotomous drug response in breast cancer cell lines
5(A): Correlate ICBP GFRN pathway predictions with ICBP drug response and make Heatmap
5(B): Correlate GFRN pathway predictions with drug response in an independent drug assay and make Heatmap
6:Differential drug response identified in GFRN phenotype heterogeneity
#Supplemental Analysis ###Supplemental Figures 3(A)-(H): Protein based pathway validations codes are in ./ASSIGN folder. Gene expression, mutation and IHC-based validations are shown here. 
## [1] "757 names have been changed"
### Supplemental Figures 4(A)-(D): Pathway activity estimates between ER+ and ER- samples in breast cancer cell lines and patient data. 
Supplemental Figures 5(A)-(G): Boxplot GFRN pathway activity across breast cancer clinical subtypes (IHC)
Supplemental Figures 6(A)-(G): Boxplot GFRN pathway activity across breast cancer intrinsic subtypes (PAM50)
Supplemental Figures 7: Graphical representation of the IHC and intrinsic subtype status distribution for ICBP cell line and TCGA breast tumors.
Supplemental Figure 8:Survival analysis with TCGA BRCA data…
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion## Call:
## survdiff(formula = Surv(tcga_clinicals$time, tcga_clinicals$vital_status) ~
## tcga_clinicals$kmeans.cluster, rho = 1)
##
## N Observed Expected
## tcga_clinicals$kmeans.cluster=Growth/BAD high 173 16.9 15.3
## tcga_clinicals$kmeans.cluster=Growth/BAD low 289 13.9 22.6
## tcga_clinicals$kmeans.cluster=Survival/HER2 high 359 33.8 28.3
## tcga_clinicals$kmeans.cluster=Survival/HER2 low 242 18.7 17.1
## (O-E)^2/E (O-E)^2/V
## tcga_clinicals$kmeans.cluster=Growth/BAD high 0.162 0.238
## tcga_clinicals$kmeans.cluster=Growth/BAD low 3.319 5.282
## tcga_clinicals$kmeans.cluster=Survival/HER2 high 1.080 1.916
## tcga_clinicals$kmeans.cluster=Survival/HER2 low 0.141 0.208
##
## Chisq= 5.5 on 3 degrees of freedom, p= 0.141
Supplemental Figure 9(A)&(B): Boxplot TCGA Principal Components across BRCA intrinsic subtypes (PAM50)
## [1] "Spearman correlation of PC1 and mean gene expression of each sample: -0.786414519142557"
Data for Supplemental Figure 10: Comparison of R2 values (proportion of variance) explained by each model for principle components (PCs) 1 through 5 from TCGA RNA-sequencing breast cancer data.
| PC | ER | ER.kmeans | ER.kmeans.pval | ER.PAM50 | ER.PAM50.pval | ER.phenotype | ER.phenotype.pval | ER.kmeans.PAM50.pval |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.0872084 | 0.1880783 | 0 | 0.1313824 | 0.0016441 | 0.1051838 | 0.0081916 | NA |
| 2 | 0.5606260 | 0.6955323 | 0 | 0.7471378 | 0.0000000 | 0.6922442 | 0.0000000 | 0.00e+00 |
| 3 | 0.0516185 | 0.3975292 | 0 | 0.2538693 | 0.0000000 | 0.2522985 | 0.0000000 | NA |
| 4 | 0.0285084 | 0.2789351 | 0 | 0.0781370 | 0.0010530 | 0.2072717 | 0.0000000 | NA |
| 5 | 0.0379096 | 0.1749349 | 0 | 0.2159659 | 0.0000000 | 0.0622188 | 0.0027064 | 2.48e-05 |
| PC | PR | PR.kmeans | PR.kmeans.pval | PR.PAM50 | PR.PAM50.pval | PR.phenotype | PR.phenotype.pval | PR.kmeans.PAM50.pval |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.0597998 | 0.1558167 | 0 | 0.1236342 | 6.03e-05 | 0.0658990 | 0.1304072 | NA |
| 2 | 0.4074544 | 0.6465939 | 0 | 0.7356092 | 0.00e+00 | 0.6263654 | 0.0000000 | 0.0e+00 |
| 3 | 0.0585759 | 0.3929508 | 0 | 0.2532850 | 0.00e+00 | 0.2241204 | 0.0000000 | NA |
| 4 | 0.0040064 | 0.2818516 | 0 | 0.0833947 | 7.60e-06 | 0.2130036 | 0.0000000 | NA |
| 5 | 0.0266319 | 0.1727616 | 0 | 0.2160601 | 0.00e+00 | 0.0402403 | 0.0261066 | 1.5e-05 |
| PC | HER2 | HER2.kmeans | HER2.kmeans.pval | HER2.PAM50 | HER2.PAM50.pval | HER2.phenotype | HER2.phenotype.pval | HER2.kmeans.PAM50.pval |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.0108250 | 0.1288564 | 0 | 0.1247560 | 0.0000000 | 0.0111700 | 0.7261904 | NA |
| 2 | 0.0000002 | 0.5087202 | 0 | 0.7246049 | 0.0000000 | 0.4222629 | 0.0000000 | 0.0000000 |
| 3 | 0.0328873 | 0.3930355 | 0 | 0.2570614 | 0.0000000 | 0.1150083 | 0.0000000 | NA |
| 4 | 0.0205140 | 0.2790501 | 0 | 0.0818977 | 0.0001486 | 0.2108924 | 0.0000000 | NA |
| 5 | 0.0234721 | 0.2073647 | 0 | 0.2238067 | 0.0000000 | 0.0315990 | 0.0865439 | 0.0068758 |
| PC | ER.PR.HER2 | ER.PR.HER2.kmeans | ER.PR.HER2.kmeans.pval | ER.PR.HER2.PAM50 | ER.PR.HER2.PAM50.pval | ER.PR.HER2.phenotype | ER.PR.HER2.phenotype.pval | ER.PR.HER2.kmeans.PAM50.pval |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.0982646 | 0.1905291 | 0 | 0.1329788 | 0.0084575 | 0.1129365 | 0.0166448 | NA |
| 2 | 0.5979224 | 0.7256479 | 0 | 0.7506555 | 0.0000000 | 0.7218103 | 0.0000000 | 0.0000000 |
| 3 | 0.0914616 | 0.4040233 | 0 | 0.2630988 | 0.0000000 | 0.2752829 | 0.0000000 | NA |
| 4 | 0.0539184 | 0.2824955 | 0 | 0.0890370 | 0.0105041 | 0.2169903 | 0.0000000 | NA |
| 5 | 0.0681934 | 0.2132864 | 0 | 0.2238788 | 0.0000000 | 0.1082416 | 0.0000891 | 0.0302152 |
| PC | kmeans | PAM50 | kmeans.PAM50 | ER.PR.HER2.PAM50.kmeans |
|---|---|---|---|---|
| 1 | 0.1244270 | 0.1229359 | 0.2100966 | 0.2207230 |
| 2 | 0.4922497 | 0.7243437 | 0.7920581 | 0.8151674 |
| 3 | 0.3845233 | 0.2489111 | 0.4695138 | 0.4784226 |
| 4 | 0.2788131 | 0.0777884 | 0.2880172 | 0.2936144 |
| 5 | 0.1725182 | 0.2159571 | 0.2904661 | 0.3047475 |
Supplemental Figure 12: Correlations between pathway activation estimates and drug response values between ER+ and ER- and between HER+ and HER2- samples in breast cancer cell lines.
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### Supplemntal Figure 13: Comparison of Lapatinib sensitivity
## The following `from` values were not present in `x`: Survival/HER2 high, Survival/HER2 low, Growth/BAD high, Growth/BAD low
This analysis was run on Fri Mar 31 02:20:00 2017